A machine learning model to predict patient survival rates at a hospital in Greenland, using a Random Forest classifier and patient data including diagnosed conditions, age, and previous medical history. Model is evaluated using the F1 Score.

Unlock the potential of agricultural production with innovative optimization techniques. Explore strategies, technologies, and practices to enhance crop yields, improve efficiency, and sustainably increase output. Revolutionize farming practices and cultivate a thriving agricultural ecosystem

"Predict and enhance employee promotions through data-driven insights and a predictive model in the 'HR Classification for Promotion' project."

This GitHub repository contains code for predicting the country destination of new Airbnb users using machine learning techniques on the "Airbnb New User Bookings" dataset from a Kaggle competition.

ICC World Cup 2023 Predictions: Unleash the power of Machine Learning to foresee cricket match outcomes. Analyze team performance and player stats for strategic insights. Your go-to resource at the intersection of sports and cutting-edge analytics.

This project uses a synthetic healthcare dataset to predict patient test results ("Normal," "Abnormal," or "Inconclusive") using machine learning. It employs ensemble methods such as Bagging, Random Forest, Boosting, and Stacking classifiers with feature engineering and preprocessing.

An end-to-end machine learning project that predicts automotive parts shortages for a major automotive manufacturing and distribution company. This project showcases data preprocessing, exploratory analysis, feature engineering, and robust predictive modeling (including XGBoost) to drive actionable insights under a strict NDA.

This project focuses on predicting NYC taxi trip durations 🚖🗽 using machine learning techniques. By analyzing factors such as pickup and drop-off locations, timestamps, and traffic conditions, it aims to provide accurate duration estimates to enhance rider and driver experiences in New York City.

A collection of data analytics and machine learning projects focused on real-world problem-solving across healthcare, sales, and public health domains.

This project predicts California housing prices using a complete machine learning workflow. It features Exploratory Data Analysis (EDA), data preprocessing, and training both a Linear Regression model and a more robust Random Forest Regressor. The final model is then optimized using GridSearchCV.

Machine learning pipeline for predicting Yellowfin Tuna weight, including data preprocessing, model training, testing, deployment, and an interactive web-based prediction interface.

this project develops a robust machine learning model to estimate house prices in the state.

Predictive modeling techniques for data-driven decision-making

This model predicts the winning probabilities for both teams during the second innings of an IPL match.

Heart disease is one of the leading causes of death worldwide. Early detection and prediction of heart disease can significantly improve treatment outcomes and save lives. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

This project delivers an end-to-end product analytics solution, leveraging Google BigQuery and Python for data engineering, comprehensive EDA, and machine learning to optimize user conversion funnels and drive growth. It identifies critical user drop-offs and provides data-backed, actionable recommendations.

This project predicts the Air Quality Index (AQI) using machine learning techniques. By analyzing historical air quality data and relevant environmental factors, it provides accurate forecasts to help raise awareness and promote healthier living conditions in urban areas.

This repository contains the data, code, and documentation for a project to analyze and predict churn in PowerCo's SME customer segment. The project includes data exploration, cleaning, and transformation, as well as the development and evaluation of a machine learning model to predict churn based on price sensitivity and other relevant factors.

This project aims to develop a machine learning model that predicts the prices of cars based on various factors such as make, model, year, mileage, engine size, and fuel type. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

This project implements a machine learning model designed to predict whether a student will graduate or drop out based on key academic and financial factors. The model uses features such as course performance, tuition fee status, and scholarship eligibility to identify strong indicators of student success.